{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[[   0.        ,   15.979     ],\n        [   0.        , 1309.76484713],\n        [   0.        , 1844.71318879],\n        [   0.        , 2491.77837707],\n        [   0.        , 3424.73525466]],\n\n       [[   0.        ,   14.179     ],\n        [   0.        ,  999.3360631 ],\n        [   0.        , 1541.45863057],\n        [   0.        , 2116.69927793],\n        [   0.        , 3444.28942247]],\n\n       [[   0.        ,   14.579     ],\n        [   0.        , 1175.57540553],\n        [   0.        , 2680.63438756],\n        [   0.        , 3838.23214015],\n        [   0.        , 4336.12771349]],\n\n       ...,\n\n       [[  15.8546    ,    0.        ],\n        [1116.55239726,    0.        ],\n        [2408.99125539,    0.        ],\n        [3300.70669622,    0.        ],\n        [4541.41532008,    0.        ]],\n\n       [[  14.4152    ,   21.6228    ],\n        [1106.09527109, 1659.14290664],\n        [2511.69827081, 3767.54740621],\n        [2961.77423194, 4442.6613479 ],\n        [3822.08191297, 5733.12286946]],\n\n       [[  15.0546    ,    0.        ],\n        [ 994.74448103,    0.        ],\n        [1770.56965617,    0.        ],\n        [2727.71725255,    0.        ],\n        [4133.00830267,    0.        ]]])"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.load('data.npy')\n",
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "financeStudent = 0\n",
    "CSStudent = 0\n",
    "fintechStudent = 0\n",
    "\n",
    "for d in data:\n",
    "    if d[0][0]==0:\n",
    "        CSStudent+=1\n",
    "    elif d[0][1]==0:\n",
    "        financeStudent+=1\n",
    "    else:\n",
    "        fintechStudent+=1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "356"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "financeStudent"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "374"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSStudent"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "270"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fintechStudent"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "finalFinance = []\n",
    "finalCS = []\n",
    "finalFintech = []"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
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array([3543.97494079,    0.        ]),\n array([   0.        , 4649.73984403]),\n array([4060.49006309,    0.        ]),\n array([3627.52998178, 5441.29497267]),\n array([3621.3724111 , 5432.05861665]),\n array([2831.69497001,    0.        ]),\n array([4978.45767014, 7467.68650522]),\n array([3657.63940358,    0.        ]),\n array([   0.        , 4044.86151328]),\n array([5177.95261863, 7766.92892795]),\n array([   0.        , 3900.05686565]),\n array([   0.        , 2878.65971897]),\n array([   0.        , 4015.59782327]),\n array([4161.5049742,    0.       ]),\n array([2664.43875217, 3996.65812825]),\n array([   0.        , 4265.66989704]),\n array([3532.29181391, 5298.43772086]),\n array([3637.89265467,    0.        ]),\n array([3639.3278057 , 5458.99170855]),\n array([3715.93536544, 5573.90304816]),\n array([   0.        , 3280.27412265]),\n array([   0.        , 4192.09708882]),\n array([   0.        , 4388.76960096]),\n array([3305.07805166, 4957.61707749]),\n array([2894.42500099,    0.        ]),\n array([4541.41532008,    0.        ]),\n array([3822.08191297, 5733.12286946]),\n array([4133.00830267,    0.        ])]"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpData = []\n",
    "for d in data:\n",
    "    tmpData.append(d[-1])\n",
    "tmpData"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "for t in tmpData:\n",
    "    if t[0]==0:\n",
    "        finalCS.append(t)\n",
    "    elif t[1]==0:\n",
    "        finalFinance.append(t)\n",
    "    else:\n",
    "        finalFintech.append(t)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalCS = np.array(finalCS)\n",
    "finalCS"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalFinance = np.array(finalFinance)\n",
    "finalFinance"
   ],
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   "execution_count": 15,
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalFintech = np.array(finalFintech)\n",
    "finalFintech"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "np.save('FinTechFinal', finalFintech, allow_pickle=True, fix_imports=True)\n",
    "np.save('FinanceFinal', finalFinance, allow_pickle=True, fix_imports=True)\n",
    "np.save('CSFinal', finalCS, allow_pickle=True, fix_imports=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
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